Using subprocesses
==================

.. py:currentmodule:: anyio

AnyIO allows you to run arbitrary executables in subprocesses, either as a one-shot call or by
opening a process handle for you that gives you more control over the subprocess.

You can either give the command as a string, in which case it is passed to your default shell
(equivalent to ``shell=True`` in :func:`subprocess.run`), or as a sequence of strings
(``shell=False``) in which case the executable is the first item in the sequence and the rest are
arguments passed to it.

.. note:: On Windows and Python 3.7 and earlier, asyncio uses :class:`~asyncio.SelectorEventLoop`
    by default which does not support subprocesses. It is recommended to upgrade to at least Python
    3.8 to overcome this limitation.

Running one-shot commands
-------------------------

To run an external command with one call, use :func:`~run_process`::

    from anyio import run_process, run


    async def main():
        result = await run_process('ps')
        print(result.stdout.decode())

    run(main)

The snippet above runs the ``ps`` command within a shell. To run it directly::

    from anyio import run_process, run


    async def main():
        result = await run_process(['ps'])
        print(result.stdout.decode())

    run(main)

Working with processes
----------------------

When you have more complex requirements for your interaction with subprocesses, you can launch one
with :func:`~open_process`::

    from anyio import open_process, run
    from anyio.streams.text import TextReceiveStream


    async def main():
        async with await open_process(['ps']) as process:
            async for text in TextReceiveStream(process.stdout):
                print(text)

    run(main)

See the API documentation of :class:`~.abc.Process` for more information.

.. _RunInProcess:

Running functions in worker processes
-------------------------------------

When you need to run CPU intensive code, worker processes are better than threads because current
implementations of Python cannot run Python code in multiple threads at once.

Exceptions to this rule are:

#. Blocking I/O operations
#. C extension code that explicitly releases the Global Interpreter Lock

If the code you wish to run does not belong in this category, it's best to use worker processes
instead in order to take advantage of multiple CPU cores.
This is done by using :func:`.to_process.run_sync`::

    import time

    from anyio import run, to_process


    def cpu_intensive_function(arg1, arg2):
        time.sleep(1)
        return arg1 + arg2

    async def main():
        result = await to_process.run_sync(cpu_intensive_function, 'Hello, ', 'world!')
        print(result)

    # This check is important when the application uses run_sync_in_process()
    if __name__ == '__main__':
        run(main)

Technical details
*****************

There are some limitations regarding the arguments and return values passed:

* the arguments must be pickleable (using the highest available protocol)
* the return value must be pickleable (using the highest available protocol)
* the target callable must be importable (lambdas and inner functions won't work)

Other considerations:

* Even ``cancellable=False`` runs can be cancelled before the request has been sent to the worker process
* If a cancellable call is cancelled during execution on the worker process, the worker process
  will be killed
* The worker process imports the parent's ``__main__`` module, so guarding for any import time side
  effects using ``if __name__ == '__main__':`` is required to avoid infinite recursion
* ``sys.stdin`` and ``sys.stdout``, ``sys.stderr`` are redirected to ``/dev/null`` so :func:`print`
  and :func:`input` won't work
* Worker processes terminate after 5 minutes of inactivity, or when the event loop is finished

  * On asyncio, either :func:`asyncio.run` or :func:`anyio.run` must be used for proper cleanup
    to happen
* Multiprocessing-style synchronization primitives are currently not available
